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ASCL Code Record

[ascl:2108.008] CatBoost: High performance gradient boosting on decision trees library

CatBoost is a machine learning method based on gradient boosting over decision trees and can be used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. It supports both numerical and categorical features and computation on CPU and GPU, and is fast and scalable. Visualization tools are also included in CatBoost.

Code site:
https://github.com/catboost/catboost
Used in:
https://ui.adsabs.harvard.edu/abs/2021arXiv210801074K
Described in:
https://ui.adsabs.harvard.edu/abs/2017arXiv170609516P
Bibcode:
2021ascl.soft08008C
Preferred citation method:

Please see citation information here: https://github.com/catboost/catboost#reference-paper


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ascl:2108.008
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